The Impacts of Neighbourhood Competition, Local Species Richness, and the Tree’s Species on Tree Health Condition in a Toronto Urban Cemetery
Bibliographic record
Abstract
Neighbourhood effects have been shown to affect the health and growth conditions of trees in natural forests and plantations. In this study, we looked into the impacts of neighbourhood competition, local species richness, and tree species on the heath conditions of urban trees in the Necropolis cemetery in Toronto. The properties examined for each tree were its visually inspected health condition, diameter at breast height, species, crown diameter, and the longitudinal and latitudinal coordinates. The distance cut-off for each neighbourhood was chosen to be 20 meters to include the rooting zones of the largest trees in the sample. Data analysis was limited to Norway maple (Acer platanoides), black locust (Robinia pseudoacacia), Norway spruce (Picea abies), Manitoba maple (Acer negundo), and silver maple (Acer saccharinum) due to the small sample sizes of other tree species. The results showed that the local species richness had a significant effect on tree health conditions with improvements to health being apparent at 4 or more species in local neighbourhoods. The focal tree’s species also had a significant effect on the health condition of focal trees. Manitoba maple and silver maple were shown to have significantly poorer health conditions than the other species, which could be due to silver maple’s over-maturing and Manitoba maple’s shade-intolerance and growing in the unmanaged sections of the park. It is recommended that the species richness of local neighbourhoods be increased, and that the trees be spaced out in order to allow sunlight to small trees, as well as mulching the small trees’ rootzones and limiting the planting of silver maple.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".